Recommender Systems Cheat Sheet
Compares content-based filtering, collaborative filtering, and matrix factorization approaches, with code examples and ranking metrics for evaluation.
2 PagesIntermediateFeb 28, 2026
Recommendation Approaches
Core paradigms for building a recommender.
- Content-based filtering- Recommends items similar to what a user liked before, based on item features (genre, tags, description)
- Collaborative filtering (user-based)- Recommends items liked by users with similar rating patterns
- Collaborative filtering (item-based)- Recommends items similar to ones the user already rated highly, based on co-rating patterns
- Matrix factorization- Decomposes the user-item rating matrix into low-rank latent user and item factor matrices (e.g., SVD, ALS)
- Hybrid- Combines content-based and collaborative signals to handle cold-start and sparsity
- Cold-start problem- Difficulty recommending for new users/items with no interaction history
Content-Based Similarity
Recommend items with similar TF-IDF text features.
python
from sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.metrics.pairwise import cosine_similaritytfidf = TfidfVectorizer(stop_words='english')tfidf_matrix = tfidf.fit_transform(items_df['description'])sim_matrix = cosine_similarity(tfidf_matrix)similar_idx = sim_matrix[item_index].argsort()[::-1][1:11] # top 10, excluding itselfrecommendations = items_df.iloc[similar_idx]['title']
Matrix Factorization with SVD
Factorize a sparse ratings matrix using scikit-learn's TruncatedSVD.
python
from sklearn.decomposition import TruncatedSVDimport numpy as np# ratings_matrix: rows = users, cols = items, 0 = missing ratingsvd = TruncatedSVD(n_components=20, random_state=42)user_factors = svd.fit_transform(ratings_matrix)item_factors = svd.components_.Tpredicted_ratings = user_factors @ item_factors.Ttop_items_for_user = np.argsort(predicted_ratings[user_id])[::-1][:10]
Evaluation Metrics
Rating-prediction accuracy vs. ranking quality.
- RMSE / MAE- Measures error between predicted and actual ratings; standard for explicit-rating tasks
- Precision@K- Fraction of the top-K recommended items the user actually interacted with
- Recall@K- Fraction of all relevant items that appear in the top-K recommendations
- NDCG- Rewards ranking relevant items higher, discounting relevance by position in the list
- Coverage- Fraction of the catalog the system is capable of recommending; guards against always recommending the same popular items
Pro Tip
Optimizing for RMSE alone doesn't guarantee good rankings -- always pair rating-error metrics with a top-K ranking metric like Precision@K or NDCG before shipping a recommender.
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